docstr: Darts' Progress Bar for `TorchForecastingModels`.
  
  Allows to customize for which model stages (sanity checks, training, validation, prediction) to display a
  progress bar.
  
  This class is a PyTorch Lightning `Callback` and can be passed to the `TorchForecastingModel` constructor
  through the `pl_trainer_kwargs` parameter.
  
  Examples
  --------
  >>> from darts.models import NBEATSModel
  >>> from darts.utils.callbacks import TFMProgressBar
  >>> # only display the training bar and not the validation, prediction, and sanity check bars
  >>> prog_bar = TFMProgressBar(enable_train_bar_only=True)
  >>> model = NBEATSModel(1, 1, pl_trainer_kwargs={"callbacks": [prog_bar]})
  
  Parameters
  ----------
  enable_sanity_check_bar
      Whether to enable to progress bar for sanity checks.
  enable_train_bar
      Whether to enable to progress bar for training.
  enable_validation_bar
      Whether to enable to progress bar for validation.
  enable_prediction_bar
      Whether to enable to progress bar for prediction.
  enable_train_bar_only
      Whether to disable all progress bars except the bar for training.
  **kwargs
      Arguments passed to the PyTorch Lightning's `TQDMProgressBar
      <https://scikit-learn.org/stable/glossary.html#term-random_state>`_.
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code: def __init__(
      self,
      enable_sanity_check_bar: bool = True,
      enable_train_bar: bool = True,
      enable_validation_bar: bool = True,
      enable_prediction_bar: bool = True,
      enable_train_bar_only: bool = False,
      **kwargs,
  ):
      """Darts' Progress Bar for `TorchForecastingModels`.
  
      Allows to customize for which model stages (sanity checks, training, validation, prediction) to display a
      progress bar.
  
      This class is a PyTorch Lightning `Callback` and can be passed to the `TorchForecastingModel` constructor
      through the `pl_trainer_kwargs` parameter.
  
      Examples
      --------
      >>> from darts.models import NBEATSModel
      >>> from darts.utils.callbacks import TFMProgressBar
      >>> # only display the training bar and not the validation, prediction, and sanity check bars
      >>> prog_bar = TFMProgressBar(enable_train_bar_only=True)
      >>> model = NBEATSModel(1, 1, pl_trainer_kwargs={"callbacks": [prog_bar]})
  
      Parameters
      ----------
      enable_sanity_check_bar
          Whether to enable to progress bar for sanity checks.
      enable_train_bar
          Whether to enable to progress bar for training.
      enable_validation_bar
          Whether to enable to progress bar for validation.
      enable_prediction_bar
          Whether to enable to progress bar for prediction.
      enable_train_bar_only
          Whether to disable all progress bars except the bar for training.
      **kwargs
          Arguments passed to the PyTorch Lightning's `TQDMProgressBar
          <https://scikit-learn.org/stable/glossary.html#term-random_state>`_.
      """
      super().__init__(**kwargs)
      self.enable_sanity_check_bar = enable_sanity_check_bar
      self.enable_train_bar = enable_train_bar
      self.enable_validation_bar = enable_validation_bar
      self.enable_prediction_bar = enable_prediction_bar
      self.enable_train_bar_only = enable_train_bar_only
  
file_path: darts/darts/utils/callbacks.py
imports: import sys
  from pytorch_lightning.callbacks import TQDMProgressBar
  from pytorch_lightning.callbacks.progress.tqdm_progress import Tqdm
label_desc: Use image node_id 1 to create a new TFMProgressBar object from inherited base classes: TQDMProgressBar with example: obj = TFMProgressBar(enable_sanity_check_bar, enable_train_bar, enable_validation_bar, enable_prediction_bar, enable_train_bar_only)